Machine learning for inversing FeO and TiO2 content on the Moon: Method and comparison

Icarus ◽  
2021 ◽  
pp. 114778
Author(s):  
Denggao Qiu ◽  
Fei Li ◽  
Jianguo Yan ◽  
Wutong Gao ◽  
Zheng Chong
Keyword(s):  
Machine Learning ◽  
The Moon ◽  
Tio2 Content

scholarly journals Unsupervised Machine Learning on Domes in the Lunar Gardner Region: Implications for Dome Classification and Local Magmatic Activities on the Moon

2021 ◽  
Vol 13 (5) ◽  
pp. 845
Author(s):  
Yuchao Chen ◽  
Qian Huang ◽  
Jiannan Zhao ◽  
Xiangyun Hu
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Formation Processes ◽  
The Moon ◽  
Unsupervised Machine Learning ◽  
Formation Stage ◽  
Fuzzy C Means Clustering ◽  
Effusion Rates ◽  
The Relationship

Lunar volcanic domes are essential windows into the local magmatic activities on the Moon. Classification of domes is a useful way to figure out the relationship between dome appearances and formation processes. Previous studies of dome classification were manually or semi-automatically carried out either qualitatively or quantitively. We applied an unsupervised machine-learning method to domes that are annularly or radially distributed around Gardner, a unique central-vent volcano located in the northern part of the Mare Tranquillitatis. High-resolution lunar imaging and spectral data were used to extract morphometric and spectral properties of domes in both the Gardner volcano and its surrounding region in the Mare Tranquillitatis. An integrated robust Fuzzy C-Means clustering algorithm was performed on 120 combinations of five morphometric (diameter, area, height, surface volume, and slope) and two elemental features (FeO and TiO2 contents) to find the optimum combination. Rheological features of domes and their dike formation parameters were calculated for dome-forming lava explanations. Results show that diameter, area, surface volume, and slope are the selected optimum features for dome clustering. 54 studied domes can be grouped into four dome clusters (DC1 to DC4). DC1 domes are relatively small, steep, and close to the Gardner volcano, with forming lavas of high viscosities and low effusion rates, representing the latest Eratosthenian dome formation stage of the Gardner volcano. Domes of DC2 to DC4 are relatively large, smooth, and widely distributed, with forming lavas of low viscosities and high effusion rates, representing magmatic activities varying from Imbrian to Eratosthenian in the northern Mare Tranquillitatis. The integrated algorithm provides a new and independent way to figure out the representative properties of lunar domes and helps us further clarify the relationship between dome clusters and local magma activities of the Moon.

scholarly journals Global mapping of lunar refractory elements: multivariate regression vs. machine learning

2019 ◽  
Vol 627 ◽  
pp. A155 ◽  
Author(s):  
M. Bhatt ◽  
C. Wöhler ◽  
A. Grumpe ◽  
N. Hasebe ◽  
M. Naito
Keyword(s):  
Machine Learning ◽  
Spatial Resolution ◽  
Lunar Regolith ◽  
Support Vector ◽  
The Moon ◽  
Spectral Parameters ◽  
Ground Truth Data ◽  
Refractory Elements ◽  
Lunar Prospector ◽  
Elemental Maps

Context.The quantitative estimation of elemental concentrations at the spatial resolution of hyperspectral near-infrared (NIR) images of the lunar surface is an important tool for understanding the processes relevant for the origin and evolution of the Moon.Aims.We aim to map the abundances of the elements Fe, Ca, and Mg at a typical accuracy of about 1 wt.% at the spatial resolution of the Moon Mineralogy Mapper (M3) instrument on-board Chandrayaan-1 lunar mission.Methods.The NIR reflectance of the lunar regolith is an integrated response to the presence of refractory elements and soil alteration processes. Our approach was to define a combination of spectral parameters that are robust with respect to the effects of soil maturity. We calibrated the spectral parameters with respect to elemental abundances measured by the Lunar Prospector Gamma Ray Spectrometer (LP GRS) and the Kaguya GRS (KGRS). For this purpose, we compared a classical multivariate linear regression (MLR) approach and the machine learning based support vector regression (SVR) technique applied to M3global observations.Results.The M3-based global elemental maps are consistent in distribution and range with the LP GRS and KGRS elemental maps and do not show artifacts in immature areas such as small fresh craters. The results derived using MLR and SVR are compared to sample-based ground truth data of the Apollo and Luna sample-return sites, where the root-mean-square deviations obtained by the two regression models are similar.Conclusions.The main advantage of the proposed new algorithm is its ability to minimize artifacts due to space-weathering effects. The elemental maps of Mg and Ca provide additional information and reveal structures not always visible in the Fe map. The global elemental abundance maps derived for the fully calibrated M3observations might thus serve as important tools to investigate the lunar geology and evolution.

The motion of a lunar orbiter

1966 ◽  
Vol 25 ◽  
pp. 373
Author(s):  
Y. Kozai
Keyword(s):  
Degrees Of Freedom ◽  
Dimensional Space ◽  
Artificial Satellite ◽  
Equations Of Motion ◽  
Triaxial Ellipsoid ◽  
The Moon ◽  
Secular Motion ◽  
The Earth ◽  
Close Satellite ◽  
The Mean

The motion of an artificial satellite around the Moon is much more complicated than that around the Earth, since the shape of the Moon is a triaxial ellipsoid and the effect of the Earth on the motion is very important even for a very close satellite.The differential equations of motion of the satellite are written in canonical form of three degrees of freedom with time depending Hamiltonian. By eliminating short-periodic terms depending on the mean longitude of the satellite and by assuming that the Earth is moving on the lunar equator, however, the equations are reduced to those of two degrees of freedom with an energy integral.Since the mean motion of the Earth around the Moon is more rapid than the secular motion of the argument of pericentre of the satellite by a factor of one order, the terms depending on the longitude of the Earth can be eliminated, and the degree of freedom is reduced to one.Then the motion can be discussed by drawing equi-energy curves in two-dimensional space. According to these figures satellites with high inclination have large possibilities of falling down to the lunar surface even if the initial eccentricities are very small.The principal properties of the motion are not changed even if plausible values ofJ3andJ4of the Moon are included.This paper has been published in Publ. astr. Soc.Japan15, 301, 1963.

Laboratory Simulation of Lunar Luminescence

1962 ◽  
Vol 14 ◽  
pp. 441-444 ◽  
Author(s):  
J. E. Geake ◽  
H. Lipson ◽  
M. D. Lumb
Keyword(s):  
Science And Technology ◽  
Laboratory Simulation ◽  
The Moon ◽  
Physics Department ◽  
Luminescent Spectrum

Work has recently begun in the Physics Department of the Manchester College of Science and Technology on an attempt to simulate lunar luminescence in the laboratory. This programme is running parallel with that of our colleagues in the Manchester University Astronomy Department, who are making observations of the luminescent spectrum of the Moon itself. Our instruments are as yet only partly completed, but we will describe briefly what they are to consist of, in the hope that we may benefit from the comments of others in the same field, and arrange to co-ordinate our work with theirs.

Formation of Lunar Craters and Bright Rays as a Result of Meteorite Impacts

1962 ◽  
Vol 14 ◽  
pp. 415-418
Author(s):  
K. P. Stanyukovich ◽  
V. A. Bronshten
Keyword(s):  
Explosive Charge ◽  
The Moon ◽  
Meteorite Impacts ◽  
The Earth ◽  
Lunar Craters ◽  
The Impact

The phenomena accompanying the impact of large meteorites on the surface of the Moon or of the Earth can be examined on the basis of the theory of explosive phenomena if we assume that, instead of an exploding meteorite moving inside the rock, we have an explosive charge (equivalent in energy), situated at a certain distance under the surface.

The Origin of the Moon

1962 ◽  
Vol 14 ◽  
pp. 149-155 ◽  
Author(s):  
E. L. Ruskol
Keyword(s):  
Chemical Composition ◽  
Solar System ◽  
The Moon ◽  
The Earth ◽  
The Difference ◽  
Origin Of The Moon

The difference between average densities of the Moon and Earth was interpreted in the preceding report by Professor H. Urey as indicating a difference in their chemical composition. Therefore, Urey assumes the Moon's formation to have taken place far away from the Earth, under conditions differing substantially from the conditions of Earth's formation. In such a case, the Earth should have captured the Moon. As is admitted by Professor Urey himself, such a capture is a very improbable event. In addition, an assumption that the “lunar” dimensions were representative of protoplanetary bodies in the entire solar system encounters great difficulties.

The Origin of the Moon and its Relationship to the Origin of the Solar System

1962 ◽  
Vol 14 ◽  
pp. 133-148 ◽  
Author(s):  
Harold C. Urey
Keyword(s):  
Solar System ◽  
The Moon ◽  
The Past ◽  
The Earth ◽  
Short Period ◽  
Origin Of The Moon

During the last 10 years, the writer has presented evidence indicating that the Moon was captured by the Earth and that the large collisions with its surface occurred within a surprisingly short period of time. These observations have been a continuous preoccupation during the past years and some explanation that seemed physically possible and reasonably probable has been sought.

A Photographic Map of the Moon

1962 ◽  
Vol 14 ◽  
pp. 113-115
Author(s):  
D. W. G. Arthur ◽  
E. A. Whitaker
Keyword(s):  
Lunar Surface ◽  
The Moon ◽  
Map Projection

The cartography of the lunar surface can be split into two operations which can be carried on quite independently. The first, which is also the most laborious, is the interpretation of the lunar photographs into the symbolism of the map, with the addition of fine details from telescopic sketches. An example of this kind of work is contained in Johann Krieger'sMond Atlaswhich consists of photographic enlargements in which Krieger has sharpened up the detail to accord with his telescopic impressions. Krieger did not go on either to convert the photographic picture into the line symbolism of a map, or to place this picture on any definite map projection.

Probable Structure and Nature of the Formations on the Reverse Side of the Moon According to Photometric Measurements of Lunar Photographs

1962 ◽  
Vol 14 ◽  
pp. 39-44
Author(s):  
A. V. Markov
Keyword(s):  
The Moon ◽  
Probable Structure ◽  
The Earth ◽  
Interplanetary Station ◽  
Photometric Measurements ◽  
Automatic Interplanetary Station

Notwithstanding the fact that a number of defects and distortions, introduced in transmission of the images of the latter to the Earth, mar the negatives of the reverse side of the Moon, indirectly obtained on 7 October 1959 by the automatic interplanetary station (AIS), it was possible to use the photometric measurements of the secondary (terrestrial) positives of the reverse side of the Moon in the experiment of the first comparison of the characteristics of the surfaces of the visible and invisible hemispheres of the Moon.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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